Decision analysis of pension options for in-married woman on German farms reveals gender disparities in pension entitlements
Alexandra Krause1, Rex Parro2, Juia Schild3, Alina Gombert4
1 Introduction
THIS IS THE FIRST TECHNICAL DRAFT
1.1 Gender equality in German farming
The ancient role model of the male and patriarch farmer is under deconstruction (Laoire 2002). When looking at agricultural study courses, practical training classes and social media, several scholars perceive a “feminization” of agriculture in Germany (Inhetveen and Schmitt 2017) - This perception is reflected in agricultural statistics, stating that woman make up more than 36 % of the agricultural workers, 23% of agricultural apprentices and 48% of agricultural students (BZL 2021). More and more innovative and successful woman farmers overcome traditional role models and claim their territory in this formerly male-dominated field (Padel 2020).
Unfortunately, this “feminization” is not reflected in the share of leadership positions and land ownership of woman. Today, only 11 % of farm managers are female and only 1/3 of land is owned by females (Destatis 2020; Tietz, Neumann, and Volkenand 2021). Presently, most woman in agriculture assume the role of the wife, contributing family or seasonal worker (Destatis 2020). As the tradition of male farm succession is still adhered to, women have little chance of gaining inherited access to farms and land (Pieper and Padel 2021). Often, women only get access to a farm by taking over a farm outside the family or marrying into it. When woman are in-married in a farm, their labor is often exploited without adequate financial remuneration. The statistically important group of in married woman in farming is expected to care for children and elderly, do domestic work (cooking, cleaning, washing, grocery shopping), manage office work and milking.
1.2 Pension of farmers’ wives in Germany
Farmers pension is challenged by structural change of traditional family farms (Glauben et al. 2009). The pension of farmers in Germany is usually a combination of agricultural insurance, leasing income from property or monthly allowance from successors (Hagedorn 1991). The possible agricultural pension entitlement is rather comparable to a pocket money and is not designed to cover the cost of living of retirees (BMEL 2021).
Farm wives can be insured by the agricultural insurance. Revenues of leasing land during retirement are an important supplement to the agricultural pension but go exclusively to the husband. In-married women usually do not own the farm(-land) and therefore depend on financial support by their husband and family. Farm property rights are unequally distributed in favor of men, which is reflected in the farm decision making process - 40 % of farmers’ wives claim that their husband decides alone on farm (AgriExperts 2019). In-married woman often work full-time on farm, lack decision making power and depend financially on their husbands property. The imbalance of a high workload on one hand without financial compensation on the other hand does not yield adequate pension entitlements. Without a proper pension plan, hard-working woman on family farms bear a high risk of old-age poverty. Acquiring independent pension entitlements is challenging for woman on farm. Their „Patchwork employment biographies“ are disadvantageous in the German pension system and often lead to a low pension entitlement.Patriarchal traditions demand child- and elderly care from woman - this workload often does not allow for noteworthy off-farm employment. As woman’s retirement is mostly based on their husband, divorce or death of husband constitute major risks for their pension.
Interventions that improve the pension of farm wives have the potential to benefit the livelihoods of family farms. In our study, we want to display different options that ensure woman farmers a safe pension entitlement. However, given the high system complexity, the long-term benefits of pension schemes are difficult to anticipate. This study aimed to integrate uncertainty into long-term performance projections for pension scheme interventions in Germany. We applied decision analysis and probabilistic modeling approaches to produce economic ex-ante assessments for for pension schemes. We compare the default option of no specific pension plan to a different pension plan options. Hereby we consider the most important pension schemes relevant for woman in agriculture, which is agricultural insurance, state insurance, private insurance and etfs. With our research we would like to encourage woman farmers to demand monetary appreciation of their work and consider risks to their family pension.
2 Methods
2.1 Identification of research priorities and model sketch
In our project, we intended to work on gender equality in German farming. After extensive literature research on woman farmers in Germany, we identified the statistically important group of in-married woman and narrowed our research question down to the pension of in-married woman in Germany.
Decision option 1 aka Default option: Woman has an apprenticeship as a gardener, marries a farmer and starts working full time on farm. She is officially registered as a farm wife and therefore pays in the agricultural insurance.
Decision option 2 The Woman pursues a pension plan to ensure financial security once she retires. Here, different sub-option are outlined. There are different options to finance the pension contrivutions and also different pension schemes to choose from. Some options are only possible with specific financing decisions: e.g. state retirement is only possible when woman has a formal working contract with her husband or another employer.
It is important to note, that our model acknowledges the contemporary legal constraints in Germany. we explicitly follow the available information on German Pension schemes as stated by pension consultants and expansionists.
Figure 2.1: Model
This plot was created with Iannone (2020).
2.2 Participatory approach
Corresponding to our Research question, we identified experts in insurance companies, the agricultural chamber, the woman farmers association as our experts. In an online community of woman in agriculture, we approached potential decision makers. After drafting a first model based on literature research and personal experiences, we contacted the experts and conducted several one-to one Interviews. The insights from with equal pay consultants and the insurance company were valuable for our system understanding. In a next step, we organized an online workshop with potential decision makers and experts. and a lively group discussion. After a lively discussion discussion with experts and decision makers, we updated our model and used the updated version for further analysis.
Figure 2.2: Invitation to the Workshop
Figure 2.3: Model before and after
2.3 Model Inputs
After receiving a calibration training in the DA-Course, we considered ourselves calibrated experts. For our input table, we estimated reasonable ranges based on a mixed approach of literature research and expert opinion. According to the Methodology of Do, Luedeling, and Whitney (2020), we tried to assign reasonable uncertainties given the long time horizon of our model.
| Description | label | variable | distribution | lower | median | upper |
|---|---|---|---|---|---|---|
| Risk_of_husband_or_family_power | Percent | Husband_risk | tnorm_0_1 | 0.4500 | NA | 0.5000 |
| Risk_of_divorce | Percent | Divorce_risk | tnorm_0_1 | 0.2000 | NA | 0.4000 |
| Risk_of_death_of_husband | Percent | Man_Death_risk | tnorm_0_1 | 0.0001 | NA | 0.0002 |
| Risk_of_bancruptcy | Percent | Bancruptcy_risk | tnorm_0_1 | 0.0100 | NA | 0.0170 |
| Risk_of_late_transfer_of_farming business | Percent | Late_transfer_risk_obstacle | tnorm_0_1 | 0.4500 | NA | 0.5000 |
| Use_child_care_options_kindergarden_nanny | requirement_for_financing | Costs_for_child_care | posnorm | 40.0000 | NA | 300.0000 |
| Use_elderly_care_options_old_peoples_home_and_24h_care_giver | requirement_for_financing | Costs_for_elderly_care | posnorm | 130.0000 | NA | 3000.0000 |
| Husbands_or_family_money | financing | Family_money | posnorm | 50.0000 | NA | 300.0000 |
| Paid_on_farm_job | financing | Farm_job_paid | posnorm | 1500.0000 | NA | 1700.0000 |
| Own_business_branch | financing | Own_branch | posnorm | 1000.0000 | NA | 1500.0000 |
| Off_farm_job | financing | Off_Farm_job | posnorm | 2200.0000 | NA | 2400.0000 |
| Agricultural_insurance | options | Agri_insurance | posnorm | 591.9400 | NA | 591.9400 |
| Agricultural_insurance_investment | options | Agri_insurance_inv | const | 241.8800 | NA | 241.8800 |
| Private_insurance | options | Private_insurance | const | 412.0000 | NA | 875.0000 |
| Private_insurance_investment | options | Private_insurance_inv | posnorm | 113.0000 | NA | 240.0000 |
| State_insurance_investment | options | State_insurance_inv | posnorm | 140.0000 | NA | 220.0000 |
| State_insurance | options | State_insurance | posnorm | 550.0000 | NA | 1100.0000 |
| ETF_cost | options | ETF_costs | posnorm | 100.5000 | NA | 1300.7000 |
| ETF | options | ETF | posnorm | 100.6000 | NA | 1300.8000 |
| Mix_cost | options | Mix_costs | posnorm | 100.7000 | NA | 1300.9000 |
| Mix | options | Mix | posnorm | 100.7000 | NA | 1300.9000 |
| Husbands_or_family_money1 | option | Default_option_2 | posnorm | 50.0000 | NA | 300.0000 |
| Agriculatural_insurance1 | options | Default_option_3 | const | 591.9400 | NA | 591.9400 |
| Agriculatural_insurance1_costs | options | Default_option_3_costs | const | 241.8800 | NA | 241.8800 |
| Coefficient of variation, ratio of the standard deviation for 40 years paying pension (death at 82) | coeff. Variation | var_cv_40 | const | 396.0000 | NA | 396.0000 |
| Coefficient of variation, ratio of the standard deviation for 17 years getting pension (death at 82) | coeff. Variation | var_cv_17 | const | 204.0000 | NA | 204.0000 |
| Coefficient of variation, ratio of the standard deviation for 17 years getting pension (death at 82) | coeff. Variation | var_cv_82 | const | 984.0000 | NA | 984.0000 |
2.4 Model inmpementation and specification
We worked together with experts to derive a conceptual model that explicitly identified all the important factors and relationships in rension schemes for in-married woman in Agriculture. Since the pension yield and input demand depend on the age of the decision-maker, all benefits and costs were modeled in relation to the age of the decision maker. Since the monthly input costs varied throughout the employment biography, reasonable time intervals were specified. Risks were quantified in the model by simulating the likelihood and consequences of events perceived as consequential for pension entitlement of woman.
The model was created with the Luedeling et al. (2021). We made use of the tools of the Wickham (2021). This report was compiled using tools from the Xie (2021) team.
3 Results
Long-term payments directed to their woman retirement incur high establishment and maintenance costs and will generate net losses in the first few years but return substantial benefits to the wife and family in the long term.
4 Discussion
Annual profits from farming mostly benefit the farm owner, which is often not the woman. Farmers likely prefer reinvesting the available money in farming technology due to the relatively early incomes and short time-lag on returns. However, structural changes in agriculture and the high give-up rates impose high insecurity on the future of the farm and may raise the awareness for proper pension plans. Uncertainties related to the communication culture in farming families, farmers’ values, farm profitability, and pension returns appeared to have the greatest influence on whether a retirement plan emerged as the preferable option. Better access to information and self confidence to ask for a fair share of farming income are prerequisites to implement pension plans for in-married woman in German farms. Narrowing these key knowledge gaps may offer additional clarity on farmers’ wives optimal course of action and provide guidance for agencies promoting insurance interventions in Germany. Our model produced a set of plausible ranges for net present values and highlighted critical variables, more clarity on which would support decision-making under uncertainty. Our research approach proved effective in providing forecasts of uncertain outcomes and can be useful for informing family farms pursuing a pension plan.
Do, Luedeling, and Whitney (2020) Fabian (2018) Müller (2010) Hadler et al. (2020) Oedl-Wieser, Schmitt, and Seiser (2020)
5 Conclusion
This is our conclusion